Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Optimizes and migrates prompts for Claude Opus 4.6's adaptive thinking, 1M context window, and advanced agentic architectures.
Automates the creation, editing, and analysis of complex Excel spreadsheets with support for dynamic formulas and financial modeling standards.
Processes and analyzes complex physiological data including ECG, EEG, EDA, and respiratory signals using the NeuroKit2 Python library.
Provides strategic guidance on AI scaling laws, capability trajectories, and product positioning for frontier models.
Provides expert insights on spatial intelligence, 3D world modeling, and AI research strategy based on Fei-Fei Li's YC talk.
Analyzes and applies François Chollet’s theoretical framework for evaluating artificial general intelligence and fluid reasoning capabilities.
Builds high-impact AI systems for scientific research using architectural principles derived from DeepMind's AlphaFold.
Builds, tests, and deploys healthcare AI models using clinical datasets and specialized medical coding systems.
Optimizes Apache Spark jobs using advanced partitioning, memory management, and shuffle tuning patterns.
Designs framework-agnostic, portable AI agents and multi-agent workflows using Oracle's Open Agent Specification.
Builds robust, production-grade backtesting systems for trading strategies with specialized handling for look-ahead bias and transaction costs.
Implements high-performance embedding pipelines and vector search strategies for RAG applications.
Generates publication-quality scientific figures and multi-panel layouts following strict journal guidelines for Nature, Science, and Cell.
Architects sophisticated LLM applications using the LangChain framework for agents, memory management, and complex tool integration.
Builds and orchestrates end-to-end MLOps pipelines from data preparation through production deployment.
Implement industry-standard prompt engineering techniques to improve LLM accuracy, reliability, and structured output handling.
Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.
Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Builds production-ready RAG systems and semantic search using optimized Gemini embedding-001 models and vector storage patterns.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and rigorous benchmarking.
Facilitates advanced computational pathology workflows by providing specialized tools for whole-slide image analysis, tissue segmentation, and spatial machine learning.
Searches and retrieves scientific preprints from arXiv.org across disciplines including Computer Science, Machine Learning, and Physics.
Automates the generation of professional Quarto PDF reports and PowerPoint presentations from CSV survey data.
Generates expertly structured German system prompts and tool integration instructions for Langdock assistants.
Automates the iterative refinement of AI agent performance through contract-driven evaluation and multi-lever optimization protocols.
Streamlines the lifecycle of LLM prompts by enabling versioning, deployment, and management within the Langfuse observability platform.
Refines AI agent performance through structured optimization loops, automated evaluation contracts, and persistent multi-lever tuning.
Generates production-ready React-Leaflet components and interactive geographic information system (GIS) interfaces using TypeScript.
Generates high-quality, customizable scientific and statistical visualizations using the foundational Python plotting library.
Transforms raw data into persuasive narratives and visualizations for business stakeholders and executive presentations.
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